SQL (Structured Query Language) is a standard language used for accessing and manipulating databases.

1: Introduction
2: Practice Dataset – Superstore
3: Data Analytics with SQL
4: Multiple Join Relationships
and Advanced Topics
6: Subqueries
UNIT 2: ORGANIZATION

2 | © 2025 True Digital Academy
In today’s lesson, we will:
1. Perform data analytics using SQL.
2. Learn about database structures and the role of structured query language (SQL).
3. Introduce basic SQL commands.
4. Explore practice data, using the Superstore Database.
DATA ORGANIZATION
LEARNING OBJECTIVES

FUNDAMENTALS OF DATABASES AND SQL
INTRODUCTION

BASIC SQL AND DATABASE
SQL (pronounced: “si-kwel”) is short for structured query language. It was
developed at IBM by Donald D. Chamberlin and Raymond Boyce in the early 1970s.
There are a number of different types of SQL database implementations and
platforms, all of which support standard (ANSI) SQL, including:
● PostgreSQL
● MySQL
● Microsoft SQL Server
● Oracle
● IBM DB2
Databases store and organize your information, and the SQL language serves as a
link between you and the information.

EXCEL/GOOGLE SHEET VS. SQL
Why do we need SQL when we have Excel/Google Sheet?
● Excel is limited by your computer’s available memory and system resources.
● Excel has a fixed upper limit of 1,048,576 rows and 16,384 columns.
● Google Sheet has a fixed limit of 10 million cells (40,000 new rows at a time) or
18,278 columns.
In other words, Excel is a local tool and Google Sheet is a cloud tool that are not able
to capably manage or interact with very large datasets. This is when SQL steps in!

What can SQL do that Excel/Google Sheet can’t?
● SQL can rapidly navigate databases and query, retrieve, and aggregate
millions of records.
● SQL is also more adept than Excel/Google Sheet at creating data flows for
cleaning and preparing data at high volumes.
● SQL is the industry standard for data query and retrieval.

However…
● SQL is not a data visualization tool.
● SQL can query and organize data, but is not typically used to analyze it.
In other words, SQL is not a substitute for Excel/Google Sheet. Instead, SQL is
normally used in conjunction with Excel/Google Sheet and other data visualization
tools when working with large data repositories.

Ok, so let’s summarize:
● Excel/Google Sheet and SQL use similar functions. For example, both tools
offer ways for you to apply methods like: COUNT, SUM, AVG, IF THEN ELSE, etc.
● But compared to spreadsheets with tons of linked tabs, SQL databases provide
users with a much more efficient way to connect related data.
● SQL is also much more scalable than Excel/Google Sheet, allowing users to
remotely interact with large datasets in production environments.

Select, import,
explore, & clean
data.
Structure, visualize
& complete
analysis.
Make
business decisions
from data.
Develop a
hypothesis- driven
approach to
analysis.
Present data-driven
insights
to audience.
BASIC SQL: ANALYTICS WORKFLOW
Obtaining data often means accessing a SQL server to retrieve the data you need.
We’ll use SQL in the second step of the GA data workflow — preparing your data:
selecting, exploring, and cleaning.

BASIC SQL: POSTGRESQL
The SQL database tool we’ll use in this class is PostgreSQL. We’ll use this because:
• It’s an object oriented relational database management system (DBMS).
• It’s powerful and standardized.
• It’s free and open source!
Note:
Many of the SQL skills we’ll learn are applicable to other
SQL database implementations. Many of the same principles
and syntax rules apply to other versions, like MS SQL, MySQL, Oracle, etc.

FUNDAMENTALS OF DATABASES AND SQL
PRACTICE DATA SET:
SUPERSTORE DATABASE

Instructions
Step 1: Go to url: https://analyticsga-
southeast2.generalassemb.ly/login?next=%2F
Step 2: Login with
Username: analytics_student@generalassemb.ly
Password: analyticsga
Step 3: Click on GA_Servers then enter password
analyticsga again (if asked)
Step 4: From list of databases, click on the database
you want to connect (In our case, it is superstore)
3
4

customers table stores customer information
- Customer ID
- Customer Name
- Segment
Note: One row represents one customer
Getting Start with the Data
Step 5: Navigate to Schemas > public > Tables
Step 6: Right click “customers” Table and select View Data > First 100
Rows
5
6

orders table stores order information such as
- Order ID
- Order date
- Ship date
- Ship mode
- Customer ID
- Product ID
- Sales
- Quantity
- Discount
- Profit
- Postal Code
- Region ID

Note:
- Each order ID was placed by one customer
- Each row represents an order of a particular product ordered by a particular customer
from any region

products table stores product information
- Product ID
- Category
- Sub category
- Product name
- Product cost to consumer
Each row represents a unique product

regions table stores region information including sales representative
- Region ID
- Country Code
- Country
- Region
- Subregion
- Salesperson
Note:
- Each row represents a country
- One salesperson could handle multiple countries

returns table stores returned order information
- Order ID
- Return Date
- Return Quantity
- Reason Returned
Note:
- Each row represents one returned order only

Schema
‣ A database schema is considered the “blueprint” of a database which describes
how the data may relate to other tables or other data models. However, the
schema does not actually contain data. The Entity-Relationship Diagram
(ER Diagram) is a tool to define a schema.

DATA ANALYTICS WITH SQL
SCENARIO
Superstore is a very large-scale shop that selling broad spectrum of products. The major
customers are in USA. Recently, furniture selling is not making profit. You, as a data
scientist, were asked to find out what went wrong and to recommend what should be the
solutions to solve this problem.

SMART Objective and Hypothesis Tree
Problem Statement
การขายเฟอร ์นิเจอร ์ไม่ทากาไร
SMART Objective
สร ้างกาไรจากการขาย
เฟอร ์นิเจอร ์อย่างน้อย x%
ภายใน 6 เดือนด้วยการศึกษา
ข้อมูลการซื้อขายในอดีต
เฟอร ์นิเจอร ์กาไรตกทุกชนิด
เลยไหม ถ้าไม่ใช่งั้นอะไรคือ
ปัญหา
Idea: เพิ่มขาย ลดทุน เท่ากับกาไร
ดังนั้นคาถามทั้งหลายจะเกี่ยวข้องกับการ
เพิ่มขายหรือการลดทุนในช่วง 1 ปีที่ผ่านมา
ของสินค้าเฟอร ์นิเจอร ์
Note: เพื่อให้ง่ายต่อการทาความเข้าใจ ใน
ตัวอย่างนี้จะโฟกัสเฉพาะใน USA เท่านัน
ทิศทางของกาไรที่ผ่านมาเป็ นอย่างไรในแต่ละปี

Q1:ทิศทางของกาไรที่ผ่านมาเป็ นอย่างไรในแต่ละปี
‣ Step 1: Think first, code later. How does the final output look like
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …

‣ Step 2: Think first, code later. What is/are the input

‣ Step 3: Think first, code later. What is the idea to process input to
the desired output
Extract year
Group By
year
Aggregate by
Summation
Sorted By
year
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …

‣ Step 4: Selecting column
Idea: We cannot select any column without knowing which table stores such columns
If we would like to pick order_date, we need to tell SQL the table name which is orders
The SELECT command:
In case of selecting all columns in a table:

‣ Step 4: Selecting column (Cont.)
• Selecting order_date from orders
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …
Desired Output
The query results are still far from the desired output.
We do not want dates, but we want years.
Let’s extract year from date using EXTRACT (YEAR FROM
order_date).

‣ Step 4: Selecting column (Cont.)
• Selecting year from orders
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …
Desired Output
The query results are getting closer to the desired output
Alias can be used to rename the column name in the query results
Another way: SELECT DATE_PART('year', order_date) AS order_year
FROM orders

‣ Step 5: Group By Year, Aggregate By Summation
Idea: Group By requires aggregation (always)
year value
2015 50
2015 60
2016 30
2016 20
2016 40
Example:
Group By year without Aggregation
Results: ??? (Computer has no idea group by what aggregation function)
Need to be aggregated into a single value
Need to be aggregated into a single value
year value
2015 110
2016 90
If summation is applied as aggregation function

‣ Step 5: Group By Year, Aggregate By Summation
SQL
Example: Group By order_date, aggregated By Summation
We are very much closer to the desired output
In our case, we want SUM(profit) as the aggregation function
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …
Desired Output

‣ Activity 1: Combine your knowledge obtained from step 4 and step 5
to complete the following processes
Extract year
Group By
year
Aggregate by
Summation
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …

‣ Activity 1: Combine your knowledge obtained from step 4 and step 5
to complete the following processes
‣ Solutions
year profit
2015 xxx
2016 xxx
2017 xxx
2018 xxx
… …
Desired Output
Good Jobs!
We get the results we want.
However, we are just lucky that
the order_year was sorted
To ensure that the order_year
was sorted in the way we want,
the ORDER BY command is
required

‣ Step 6: Sorted by order_date
The default sorting mode is ascending
Use ASC for ascending sort
Use DESC for descending sort
Let’s apply sorting to complete the Q1
Discussions: What’s data telling you?
Answer: The annual profit was increasing continuously except 2020
Be careful: DO NOT too hurry to conclude that we are having problem in 2020 because the data we have may not be
collected from a whole 2020

‣ Let’s check
• When was the latest order in our data
Idea:
Use MAX to get the maximum value in a given column
Use MIN to get the minimum value in a given column
It is cleared that data in 2020 is only from January

‣ Activity 2: Checking whether we have data in a whole 2015 or not
Too small number. That’s why we need to check

‣ Activity 2: Checking whether we have data in a whole 2015 or not
Solutions
It is cleared that we have only data from December in 2015

Problem Statement
SMART Objective
ทิศทางของกาไรจากการขายเฟอร ์นิเจอร ์ที่ผ่านมาเป็ นอย่างไรในแต่ละปี
ปัญหา

Q2: ทิศทางของกาไรจากการขายเฟอร ์นิเจอร ์ที่ผ่านมาเป็ นอย่างไรในแต่ละปี
year profit category
2015 xxx Furniture
2016 xxx Furniture
2017 xxx Furniture
2018 xxx Furniture
… … ….
This column is optional to display
because we can query only order related
to furniture
Displaying it, perhaps, useful in the
sense of ensuring that we performed the
right query

‣ Step 2: Think first, code later. What is/are input
Joining 2 tables (orders and products) via the common field

‣ Get to know SQL JOIN
Location Temperature
Bangkok 35
Rayong 34
Phuket 33
Prae 28
Location Area sq.km
Bangkok 1,569
Chonburi 4,363
Phuket 543
Chiang Rai 11,678
Location
Bangkok 35
Rayong 34
Phuket 33
Prae 28
Area sq.km
1,569
NULL
543
NULL
Output
Example

Bangkok 35
Rayong 34
Phuket 33
Prae 28
Location Area sq.km
Bangkok 1,569
Chonburi 4,363
Phuket 543
Chiang Rai 11,678
Location
Output
Location Area sq.km
Bangkok 1,569
Chonburi 4,363
Phuket 543
Chiang Rai 11,678
Temperature
35
NULL
33
NULL
Example

Bangkok 35
Rayong 34
Phuket 33
Prae 28
Location Area sq.km
Bangkok 1,569
Chonburi 4,363
Phuket 543
Chiang Rai 11,678
Location
Output
Bangkok 35
Phuket 33
Area sq.km
1,569
543
Example

Bangkok 35
Rayong 34
Phuket 33
Prae 28
Location Area sq.km
Bangkok 1,569
Chonburi 4,363
Phuket 543
Chiang Rai 11,678
Location
Output
Bangkok 35
Rayong 34
Phuket 33
Prae 28
Chonburi NULL
Chiang Rai NULL
Area sq.km
1,569
NULL
543
NULL
4,363
11,678
Example

‣ Step 3: Joining orders and products
• Question: Which JOIN should be applied
• Answer: Depends on the unit of analysis
Original Question:
orders
Each row represents an order of a particular product ordered by a particular customer from any region
products
Each row represents a unique product
The unit of analysis to answer the original question is an order a particular product
The proper joining are one of the following options
1. LEFT join (orders, products)
2. RIGHT JOIN (products, orders)
3. INNER JOIN (orders, products)
4. INNER JOIN (products, orders)

‣ Step 3: Joining orders and products (cont.)
• Let’s start with the LEFT JOIN
From orders table From products table

‣ Now we have got the input table
‣ Step 4: Think first code later. How to process input to the desired
output
year profit category
2015 xxx Furniture
2016 xxx Furniture
2017 xxx Furniture
2018 xxx Furniture
… … ….
Desired output
Extract year
Group By
year and
category
Aggregate by
Summation
Filter only
Furniture
Sorted by
year

‣ Step 4: Select columns
• order_date: Extracting year from order
date
• Category
• profit
We have modified the previous query in step 3
This is still not answering the original question.
We need further processes

‣ Step 5: Group By and Aggregation
• GROUP BY: Year and Category
• Aggregation: Sum of profit
We only want Furniture category.
Let’s filter others out using HAVING

‣ Step 6: Filter other categories out. Keep only Furniture
• HAVING is used to filter measures you’ve aggregated
Year Values
2019 100
2019 120
2020 200
2020 180
annual
SUM
SUM
Year Values
2019 220
2020 380 HAVING Year=2019
Year Values
2019 220
Basic Idea

‣ Step 6: Filter other categories out. Keep only Furniture
This query results is very close to our desired output
We only need to sort by year

‣ Step 7: Apply sorting Good jobs! We have completed to process data from input to
the desired output. This is the heart of programming which is
the logic to process input to the desired output.
Question: What is the results telling you?
Note: We have already known that 2015 and 2020 are not
full year data
Answer: The cumulative profit of furniture selling is growing
up in each year
Let’s move to the next analytic question

‣ Recall from step 3, we have 4 possible JOINs for this problem
The proper joining are one of the following options
1. LEFT join (orders, products)
2. RIGHT JOIN (products, orders)
3. INNER JOIN (orders, products)
4. INNER JOIN (products, orders)
You may check the results
1 2
3
4

Problem Statement
SMART Objective
ปัญหา
ทิศทางของกาไรจากการขายเฟอร ์นิเจอร ์ที่ผ่านมาเป็ นอย่างไรในแต่ละ
sub region ที่ USA ในแต่ละเดือนของปี 2019

Q3: ทิศทางของกาไรจากการขายเฟอร ์นิเจอร ์ที่ผ่านมาเป็ นอย่างไรในแต่ละ sub region ที่ USA ในแต่ละเดือนของปี 2019
year Month profit category subregion country
2019 1 xxx Furniture … United States
2019 2 xxx Furniture … United States
2019 … xxx Furniture … United States
2019 … xxx Furniture … United States
… … … …. … United States

Joining 2 tables (orders and regions) via the common field
We need these columns

‣ Activity 3: Use your knowledge from previous questions to merge 3 tables
(orders, products, regions) together and select the following columns
• Orders
• Order_date
• Extract month
• Extract year
• Profit
• Products
• Category
• Regions
• Sub_region
• Country

‣ Activity 3: Use your knowledge from previous questions to merge 3 tables
(orders, products, regions) together and select the following columns
Solutions

‣ Now we have the input from the activity 3
output
year Month profit categor
y
subregi
on
country
2019 1 xxx Furniture … United
States
2019 2 xxx Furniture … United
States
2019 … xxx Furniture … United
States
2019 … xxx Furniture … United
States
… … … …. … United
States
Group By all
except profit
Aggregate by
Summation
Filter by
Furniture, United
States and 2019
Sorting

• Group BY all except profit
• Aggregate profit by summation
We have modified the previous query in activity 3

‣ Step 5: Filtered by Furniture, United States and 2019

‣ Step 6: Sorting
• Tips:
• We are looking for non-profitable evidences
• Apply sorting directly to cum_profit returns the serious non-profitable evidences, but we
will loss the trend monitoring
• Trade-off between focus to the non-profitable evidences and trend monitoring

‣ Step 6: Sorting
• Option 1: Focus on non-profitable evidences Question: What is the results telling you?
Answer: There are several non-profitable
evidences in 2019, but June (East and South),
July (West), and February (Central) seems to be
very seriously non-profitable evidences
We need to further investigate what went wrong in
those evidences

‣ Step 6: Sorting
• Option 2: Focus
on trend
monitoring
Question: What do the results tell
you?
Answer: The cumulative profit, for
example, in 2019 at Central USA is
fluctuating. There are some non-
profitable evidences in Feb, Apr,
and Oct.
We need to further investigate what
went wrong in those evidences and
other subregions

Problem Statement
SMART Objective
ปัญหา
Product ใดในกลุ่มเฟอร ์นิเจอร ์ที่ขายแล้วขาดทุนจาแนกตาม
subregion ใน USA เฉพาะปี 2019
ทิศทางของกาไรจากการขายเฟอร ์นิเจอร ์ที่ผ่านมาเป็ นอย่างไรในแต่ละ sub region ที่ USA ใน
แต่ละเดือนของปี 2019

Q4: Product ใดในกลุ่มเฟอร ์นิเจอร ์ที่ขายแล้วขาดทุนจาแนกตาม subregion ใน USA เฉพาะปี 2019
year Month profit category subcategory Product name subregion country
2019 1 xxx Furniture … … … United States
2019 2 xxx Furniture … … … United States
2019 … xxx Furniture … … … United States
2019 … xxx Furniture … … … United States
… … … …. … … … United States
Must be negative values

Joining 2 tables (orders and regions) via the common field

‣ Activity 4: Use your knowledge from previous questions to merge 3 tables (orders, products, regions)
together and select the following columns
• Orders
• Order_date
• Extract month
• Extract year
• Profit
• Products
• Category
• Subcategory
• Product name
• Regions
• Sub_region
• Country

‣ Activity 4: Use your knowledge from previous questions to merge 3 tables (orders, products,
regions) together and select the following columns
Solutions

‣ Now we have input
output
Group By all
except profit
Aggregate by
Summation
Filter by Furniture,
United States, 2019,
and negative profit
Sorting
year Mon
th
prof
it
cate
gory
sub
cate
gory
Pro
duct
nam
e
subr
egio
n
cou
ntry
2019 1 xxx Furni
ture
… … … Unit
ed
Stat
es
2019 2 xxx Furni
ture
… … … Unit
ed
Stat
es
2019 … xxx Furni
ture
… … … Unit
ed
Stat
es
… … … …. … … … Unit
ed
Stat
es

• Group BY all except profit
• Aggregate profit by summation

‣ Step 5: Filtered the group with Furniture, United States, 2019, and
negative cumulative profit

‣ Step 6: Sorting
• Pinpoint to which products and their non-profitable evidences
• Sorting priority focus on cumulative profit
• Rank top 10 of the non-profitable evidences
‣ The LIMIT command is used for displaying only “nrows” of the query
results (works only in PostgresQL)

‣ Step 6: Sorting (Cont.)
Question: What is the results telling us?
Answer: 3 subcategories (table, chair, bookcases) were found
with serious non-profitable evidences

Problem Statement
SMART Objective
ปัญหา
ตัวอย่างนี้จะโฟกัสเฉพาะใน USA เท่านัน Product ใดในกลุ่มเฟอร ์นิเจอร ์ที่ขายแล้วขาดทุนจาแนกตาม
Product ที่มี serious non-profitable evidences มีการลด
ราคาอย่างไร เช่น Sauder Classic Bookcases, Metal ที่มี
serious non-profitable evidence หนักที่สุด ใน United
States ในปี 2019
โครงสร ้างของราคาผิดปกติ
หรือไม่

Q5: Product ที่มี serious non-profitable evidences มีการลดราคาต่างจาก Product อื่นอย่างไร
Year Month Product Name SUM Quantity AVG Sales AVG Discount SUM profit
2019 … … … … … …
2019 … … … … … …
2019 … … … … … …
2019 … … … … … …
2019 … … … … … …

We need these columns Joining 2 tables (orders and regions) via the common field

• Orders
• Order_date
• Extract Year/ Month
• Quantity
• Sales
• Discount
• Profit
• Products
• Product Name
• Regions
• Country

output
Group By all
except profit
Aggregate by
SUM and
Average
Filter by Furniture, United
States, 2019, and
product_name ‘Saunder
Classic Bookcase, Metal’ Sorting
Year Mont
h
Product
Name
SUM Quantity AVG Sales AVG
Discount
SUM profit
2019 … … … … … …
2019 … … … … … …
2019 … … … … … …
2019 … … … … … …
2019 … … … … … …

‣ Filtering, WHERE command is used for filtering non-aggregated data
locations Temperature
Bangkok 35
Rayong 34
Phuket 33
Prae 28
locations Temperature
Bangkok 35

‣ Step 4: Filtering United States, 2019 and product name using WHERE
“Sauder Classic Bookcase, Metal” was picked for
investigation because it has the most serious non-
profitable evidences
Try LIKE keyword LIKE “Sauder%” or ILIKE for
PostgresQL

‣ Step 5: Group By and Aggregation (remove select country)
• Group By all except quantity, profit, discount, and sales
• Aggregate by average and summation

‣ Step 6: Sorting
Question: What the results telling you
Answer: Something wrong with the discount. In
common sense, the more purchase quantity the more
discount can be.
However, we observed that order in February has
average discount about half of October, but the
purchasing quantity shows contradiction to the common
sense.
Moreover, cumulative profit in February is obviously
extreme compared to others.
Only average value may not be able to reveal fact.
Perhaps, statistical parameters could help.

‣ Step 7: Add some statistical parameters to sales
• Min
• Max
• Standard deviation
Answer: It tells not much for this case. We
observe that the non-profitable evidences
have constant selling price
Be careful, do not hurry to conclude because
statistics could mislead to you.
Further investigation in transaction level on
specific product and time is required

‣ Statistical could mislead you

Problem Statement
SMART Objective
ปัญหา
ตัวอย่างนี้จะโฟกัสเฉพาะใน USA เท่านัน Product ใดในกลุ่มเฟอร ์นิเจอร ์ที่ขายแล้วขาดทุนจาแนกตาม
Product ที่มี serious non-profitable evidences มีการ
ลดราคาอย่างไร เช่น Sauder Classic Bookcases, Metal
ที่มี serious non-profitable evidence หนักที่สุด ใน
United States ในปี 2019
โครงสร ้างของราคาผิดปกติ
หรือไม่
Product ที่มี serious non-profitable evidences มี
transaction ผิดปกติหรือไม่ แต่ละ transaction มี profit
margin เท่าใด เช่น Sauder Classic Bookcases, Metal ที่มี
serious non-profitable evidence หนักที่สุดใน United
States ในปี 2019

Q6: Product ที่มี serious non-profitable evidences มี transaction ผิดปกติหรือไม่ แต่ละ transaction มี profit margin เท่าใด
year month Order ID Product name quantity sales discount profit Profit margin

We need these columns Joining 2 tables (orders and regions) via the common field

‣ Activity 6: Use your knowledge from previous questions to merge 3
tables (orders, products, regions) together and select the following
columns
‣ Select only relevant columns defined in step 2

‣ Activity 6: Use your knowledge from previous questions to merge 3 tables (orders, products, regions)
together and select the following columns
‣ Select only relevant columns defined in step 2
Solution

‣ Step 3: Think first code later. How to process input to the desired output
Calculate profit
margin
Filter by Furniture,
United States, 2019,
and product_name
Sorting
year month Order ID Product name quantity sales discount profit Profit margin

‣ Step 4: Calculate profit margin

‣ Step 4: Calculate profit margin
• Step 4.1 Apply decimal round to make display results easier to read
We have modified the previous query in the previous step 4

‣ Step 5: Filtering United States, 2019, and Sauder Classic Bookcase,
Metal
We have modified the previous query in the previous step 4.1

‣ Step 6: Sorting
Answer: For Sauder Classic Bookcase, Metal sold in
2019 at USA, overall transactions look fine except IT-
2019-3150765 that has the same quantity, sales, and
discount as other February transactions, but profit looks
very abnormal, and this transaction is the most serious
non-profitable evidence.
We do not have any further data to investigate what
exactly went wrong about it. We could suspect that might
involve with human error, something outside the regular
purchasing system, etc.

DATA ORGANIZATION
CONCLUSION

SQL IN A NUTSHELL
✔ SELECT: Selects the columns.
✔ FROM: Points to the table.
✔ WHERE: Filters on rows.
✔ GROUP BY: Aggregates across values of a variable.
✔ HAVING: Filters groups.
✔ ORDER BY: Sorts or arranges the results.
✔ LIMIT: Limits result to the first n rows.
Also: (INNER/LEFT/RIGHT/FULL)JOIN: Combine columns across tables with
the relationships.

DATA ORGANIZATION WITH SQL
HOMEWORK 3
(กาหนดส่งก่อนเรียนครั้ง
หน้า 29 มี.ค.)

DATA ORGANIZATION
DIRECTIONS
‣ ทำตำมสไลด์ที่เหลือ (slide 87 – 96)
‣ ตั้งคำถำมเพิ่ม 1 คำถำมนอกเหนือจำกคำถำมในห้อง พร้อมทั้งตอบคำถำมด้วย SQL โดยต้องมีกำรใช้ GROUP BY +
Aggregation
DELIVERABLES
1. ส่ง SQL statement, พร้อม capture หน้ำจอกำรใช้งำนและตัวอย่ำงผลลัพธ์ที่ได้ อธิบำยสิ่งที่ได้โดยสังเขป(คร่ำวๆ)
2. ส่งเป็น PDF
3. ตั้งชื่อไฟล์ขึ้นต้นด้วย HW3_ชื่อนำมสกุล หย่อนลงใน Google Drive โฟลเดอร์ชื่อนำมสกุล ตนเอง

Exit Ticket (เลือก Week 3: 22-Mar-25)
https://bit.ly/4gKS4x9

SQL (Structured Query Language) is a standard language used for accessing and manipulating databases.

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SQL (Structured Query Language) is a standard language used for accessing and manipulating databases.